A Quantitative Model of Energy Release and Heating by Time-dependent, Localized Reconnection in a Flare with a Thermal Loop-top X-ray Source

We present a quantitative model of the magnetic energy stored and then released through magnetic reconnection for a flare on 26 Feb 2004. This flare, well observed by RHESSI and TRACE, shows evidence of non-thermal electrons only for a brief, early phase. Throughout the main period of energy release there is a super-hot (T>30 MK) plasma emitting thermal bremsstrahlung atop the flare loops. Our model describes the heating and compression of such a source by localized, transient magnetic reconnection. It is a three-dimensional generalization of the Petschek model whereby Alfven-speed retraction following reconnection drives supersonic inflows parallel to the field lines, which form shocks heating, compressing, and confining a loop-top plasma plug. The confining inflows provide longer life than a freely-expanding or conductively-cooling plasma of similar size and temperature. Superposition of successive transient episodes of localized reconnection across a current sheet produces an apparently persistent, localized source of high-temperature emission. The temperature of the source decreases smoothly on a time scale consistent with observations, far longer than the cooling time of a single plug. Built from a disordered collection of small plugs, the source need not have the coherent jet-like structure predicted by steady-state reconnection models. This new model predicts temperatures and emission measure consistent with the observations of 26 Feb 2004. Furthermore, the total energy released by the flare is found to be roughly consistent with that predicted by the model. Only a small fraction of the energy released appears in the super-hot source at any one time, but roughly a quarter of the flare energy is thermalized by the reconnection shocks over the course of the flare. All energy is presumed to ultimately appear in the lower-temperature T<20 MK, post-flare loops

Seasonal phytoplankton blooms in the North Atlantic linked to the overwintering strategies of copepods

The North Atlantic Ocean contains diverse patterns of seasonal phytoplankton blooms with distinct internal dynamics. We analyzed blooms using remotely-sensed chlorophyll a concentration data and change point statistics. The first bloom of the year began during spring at low latitudes and later in summer at higher latitudes. In regions where spring blooms occurred at high frequency (i.e., proportion of years that a bloom was detected), there was a negative correlation between bloom timing and duration, indicating that early blooms last longer. In much of the Northeast Atlantic, bloom development extended over multiple seasons resulting in peak chlorophyll concentrations in summer. Spring bloom start day was found to be positively correlated with a spring phenology index and showed both positive and negative correlations to sea surface temperature and the North Atlantic Oscillation in different regions. Based on the characteristics of spring and summer blooms, the North Atlantic can be classified into two regions: a seasonal bloom region, with a well-defined bloom limited to a single season; and a multi-seasonal bloom region, with blooms extending over multiple seasons. These regions differed in the correlation between bloom start and duration with only the seasonal bloom region showing a significant, negative correlation. We tested the hypothesis that the nearsurface springtime distribution of copepods that undergo diapause (Calanus finmarchicus, C. helgolandicus, C. glacialis, and C. hyperboreus) may contribute to the contrast in bloom development between the two regions. Peak near-surface spring abundance of the late stages of these Calanoid copepods was generally associated with areas having a well-defined seasonal bloom, implying a link between bloom shape and their abundance. We suggest that either grazing is a factor in shaping the seasonal bloom or bloom shape determines whether a habitat is conducive to diapause, while recognizing that both factors can re-enforce each other

Extent and Causes of Chesapeake Bay Warming

Coastal environments such as the Chesapeake Bay have long been impacted by eutrophication stressors resulting from human activities, and these impacts are now being compounded by global warming trends. However, there are few studies documenting long-term estuarine temperature change and the relative contributions of rivers, the atmosphere, and the ocean. In this study, Chesapeake Bay warming, since 1985, is quantified using a combination of cruise observations and model outputs, and the relative contributions to that warming are estimated via numerical sensitivity experiments with a watershed–estuarine modeling system. Throughout the Bay’s main stem, similar warming rates are found at the surface and bottom between the late 1980s and late 2010s (0.02 +/- 0.02C/year, mean +/- 1 standard error), with elevated summer rates (0.04 +/- 0.01C/year) and lower rates of winter warming (0.01 +/- 0.01C/year). Most (~85%) of this estuarine warming is driven by atmospheric effects. The secondary influence of ocean warming increases with proximity to the Bay mouth, where it accounts for more than half of summer warming in bottom waters. Sea level rise has slightly reduced summer warming, and the influence of riverine warming has been limited to the heads of tidal tributaries. Future rates of warming in Chesapeake Bay will depend not only on global atmospheric trends, but also on regional circulation patterns in mid-Atlantic waters, which are currently warming faster than the atmosphere. Supporting model data available at: https://doi.org/10.25773/c774-a36

An Observational Overview of Solar Flares

We present an overview of solar flares and associated phenomena, drawing upon a wide range of observational data primarily from the RHESSI era. Following an introductory discussion and overview of the status of observational capabilities, the article is split into topical sections which deal with different areas of flare phenomena (footpoints and ribbons, coronal sources, relationship to coronal mass ejections) and their interconnections. We also discuss flare soft X-ray spectroscopy and the energetics of the process. The emphasis is to describe the observations from multiple points of view, while bearing in mind the models that link them to each other and to theory. The present theoretical and observational understanding of solar flares is far from complete, so we conclude with a brief discussion of models, and a list of missing but important observations.Comment: This is an article for a monograph on the physics of solar flares, inspired by RHESSI observations. The individual articles are to appear in Space Science Reviews (2011

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature

Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: A narrative review

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD. © 2020 EDIZIONI MINERVA MEDIC

3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0

Background and Purpose: Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. Methods: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. Results: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%–10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p &lt; 0.0001) and 95.66%, 0.956 (p &lt; 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p &lt; 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p &lt; 0.0001). It took &lt;2 s to perform the online CADx process on a supercomputer. Conclusions: The performance order of the three AI systems was DCNN &gt; tCNN &gt; ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization. © 2020 Elsevier Lt